Optimum Delivery Plan for Trucks in Urban Area under Environmental Restrictions Using Probedata

本研究旨在揭示基于探测数据的运输活动的基本特征，这些数据是由安装在卡车上的一套探测设备（包括GPS和数据记录器）收集的。根据这些探测数据，我们制定了数学模型，以制定环境限制下卡车的交付计划。从降低货车运营成本和改善城市环境的角度出发，提出了不同货车的配送方案，并进行了评价。本研究的主要成果如下：首先，考虑基于探测器数据获得的行程时间和装卸时间的不确定性，建立了确定最佳出发时间的数学模型，以制定指定的交货时间和选择最优的交货路线；本文还推导了影响道路网络中卡车路线选择的一些因素，并以揭示的因素作为解释变量建立了离散路径选择模型。其次，我们针对不同的卡车制定了两种运输计划（送货上门服务车和货运集装箱拖车）。1)提出了在货物OD需求下配送中心的配送方案，并从提高配送效率和减少对环境的负面影响两方面进行了评价。2)利用本文建立的路线选择模型，估计了收费政策对卡车（挂车）从城市地区、环境脆弱地区的高速公路转向沿海地区的高速公路以改善道路环境的影响。

This study aims to reveal the basic characteristics of delivery activities based on probe date which were gathered by a set of probe equipment (comprising GPS and data logger) mounted to trucks. With these probe data, we formulated mathematical models to lay out delivery plans for trucks under environmental restrictions. Then some delivery plans for different trucks were proposed and evaluated from the viewpoints of both reduction in truck operation cost and improvement of urban environment. The main findings of this study are summarized as follows:First, we built mathematical models for deciding optimum departure time to make the designated delivery time and choosing optimum delivery route by considering the uncertainty of travel time and loading/unloading time obtained based on probe data. We also deducted some factors influencing route choice by trucks on road networks and developed disaggregate route choice models using revealed factors as explanatory variables.Second, we made two types of delivery plans for different trucks (home delivery service trucks and freight container trailer trucks). 1) We proposed allocation plans of distribution centers for home delivery trucks under the cargo OD demand, and evaluated them from improvement of efficiency of truck delivery service as well as reduction in negative environmental impacts. 2) We estimated the effects of toll policy on changing trucks' (trailer trucks) running routes from highways in urban area, environmentally vulnerable area, to expressway in coastal area to improve roadside environment, using our route choice models.